Marine Object Detection Based on Top-View Scenes Using Deep Learning on Edge Devices

Abstract

Marine object detection and tracking is an important application for several disciplines such as sea surface monitoring, marine area management, ship collision avoidance, search and rescue missions, etc. Top-view scenes based on aerial or satellite imaging offer capturing objects from new angles of view or for locations that are not seen by capturing nodes fixed at the port side or mounted on moving boats. Moreover, artificial intelligence techniques based on deep learning provide robust solutions for classification and detection. Convolutional neural network (CNN) architectures are being used to detect multiple objects in images and videos. The achieved performance proves the relevance of CNNs in circumventing existing computer vision challenges. In this paper, we investigate the state-of-the-art CNN-based technique, so called You only look once (YOLO), to detect marine objects in images showing sea ships and humans from top-view. YOLO available models are trained using our collected dataset. The evaluation of the trained models illustrates the effectiveness of YOLO in detecting targeted classes (humans and sea ships) with high precision (90 %). The deployment of the trained model on embedded edge devices achieves a high inference performance beyond 80 frames per second.